Recent world events have accelerated the evolution of the US military from monolithic formations arrayed against a known enemy, to a force that must respond to rapidly changing world events. New technologies are part of the Army's evolution and thermal imaging sensors are becoming more and more prevalent on the modern battlefield. These sensors are integrated into advanced weapon systems or commonly used for battlefield surveillance. Thermal imaging systems give the soldier the ability to deliver deadly force onto an enemy at long ranges at any time of day or night. The ability to differentiate friendly and threat forces in this situation is critical for the avoidance of friendly fire incidents and for the proper use of battlefield resources. The ability to foresee the location of the Army's next battlefield is becoming more difficult, and we don't know where the next battlefield will be from year to year. Infrared target recognition training tools need to be flexible, adaptable, and be based on not only the latest intelligence data but have geographically specific training available to the soldier. To address this training issue, personnel of the Measurement and Signatures Division at the National Ground Intelligence Center have created the Simulated Infrared Earth Environment Lab (SIREEL) web site. The SIREEL web site contains extensive infrared signature data on numerous threat and friendly vehicles and the site is designed to provide country-specific vehicle identification training in support of US military deployments. The bulk of the content currently on the site consists of infrared signature data collected over a decade of intelligence gathering. The site also employs state of the art infrared signature modeling capabilities to provide the soldier in training the most flexible training possible. If measured data on a vehicle is not available, the website developers have the capability to calculate the infrared signature of ground vehicles in any location, any type of terrain, any weather condition, any operational state, at any time of day on any day of the year. This allows the SIREEL website developers to completely populate target signature training databases when measured data is unavailable for required vehicles. This paper explores the methodologies and tools necessary to provide the predictive infrared ground vehicle signatures for this application.
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